We investigated the feasibility of utilizing the normalized characteristic frequencies for diagnosing the defective roller bearings in case of fluctuant rotating speeds. The time-frequency distributions of the envelope signals of the vibration data were constructed through the Empirical mode decomposition (EMD) as well as the instantaneous frequency calculation. The bearing defect-related frequencies were then normalized with respect to the instantaneous rotation frequency of the shaft so that the factor of the rotating speed fluctuation was removed; thus the characteristic frequencies of bearing malfunctions could be observed in terms of constant values. The magnitude distributions of the marginal envelope spectra at the corresponding normalized bearing defect-related frequencies were extracted as the feature vectors. The Support vector machine (SVM) was used to classify the extracted feature vectors of different bearing fault classes. A test rig of roller bearing system was performed to illustrate the different bearing faults, including different levels of inner race defect, outer race defect and roller defect. The analysis results demonstrate the capability and effectiveness of the proposed approach for accurately identifying the bearing defects in case of fluctuant rotating speed.